Overview

Dataset statistics

Number of variables30
Number of observations1100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory244.4 MiB
Average record size in memory233.0 B

Variable types

Numeric17
Categorical12
Boolean1

Alerts

fuel_tank_volume has a high cardinality: 139 distinct values High cardinality
listed_date has a high cardinality: 1212 distinct values High cardinality
model_name has a high cardinality: 656 distinct values High cardinality
city_fuel_economy is highly correlated with engine_displacement and 8 other fieldsHigh correlation
daysonmarket is highly correlated with listing_idHigh correlation
engine_displacement is highly correlated with city_fuel_economy and 7 other fieldsHigh correlation
height is highly correlated with city_fuel_economy and 7 other fieldsHigh correlation
highway_fuel_economy is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
horsepower is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
is_new is highly correlated with mileage and 1 other fieldsHigh correlation
length is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
listing_id is highly correlated with daysonmarketHigh correlation
mileage is highly correlated with is_new and 1 other fieldsHigh correlation
price is highly correlated with city_fuel_economy and 7 other fieldsHigh correlation
wheelbase is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
width is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
year is highly correlated with is_new and 2 other fieldsHigh correlation
Torque_lb_ft is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
back_legroom is highly correlated with heightHigh correlation
city_fuel_economy is highly correlated with engine_displacement and 7 other fieldsHigh correlation
daysonmarket is highly correlated with listing_idHigh correlation
engine_displacement is highly correlated with city_fuel_economy and 6 other fieldsHigh correlation
height is highly correlated with back_legroom and 8 other fieldsHigh correlation
highway_fuel_economy is highly correlated with city_fuel_economy and 7 other fieldsHigh correlation
horsepower is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
is_new is highly correlated with mileage and 1 other fieldsHigh correlation
length is highly correlated with city_fuel_economy and 7 other fieldsHigh correlation
listing_id is highly correlated with daysonmarketHigh correlation
mileage is highly correlated with is_new and 1 other fieldsHigh correlation
price is highly correlated with horsepower and 1 other fieldsHigh correlation
wheelbase is highly correlated with city_fuel_economy and 7 other fieldsHigh correlation
width is highly correlated with city_fuel_economy and 6 other fieldsHigh correlation
year is highly correlated with is_new and 1 other fieldsHigh correlation
Torque_lb_ft is highly correlated with city_fuel_economy and 8 other fieldsHigh correlation
city_fuel_economy is highly correlated with engine_displacement and 6 other fieldsHigh correlation
daysonmarket is highly correlated with listing_idHigh correlation
engine_displacement is highly correlated with city_fuel_economy and 5 other fieldsHigh correlation
height is highly correlated with city_fuel_economy and 1 other fieldsHigh correlation
highway_fuel_economy is highly correlated with city_fuel_economy and 4 other fieldsHigh correlation
horsepower is highly correlated with city_fuel_economy and 6 other fieldsHigh correlation
is_new is highly correlated with mileage and 1 other fieldsHigh correlation
length is highly correlated with city_fuel_economy and 4 other fieldsHigh correlation
listing_id is highly correlated with daysonmarketHigh correlation
mileage is highly correlated with is_new and 1 other fieldsHigh correlation
wheelbase is highly correlated with city_fuel_economy and 5 other fieldsHigh correlation
width is highly correlated with horsepower and 2 other fieldsHigh correlation
year is highly correlated with is_new and 1 other fieldsHigh correlation
Torque_lb_ft is highly correlated with city_fuel_economy and 6 other fieldsHigh correlation
maximum_seating is highly correlated with body_typeHigh correlation
fuel_type is highly correlated with engine_cylindersHigh correlation
engine_cylinders is highly correlated with fuel_typeHigh correlation
transmission_display is highly correlated with transmissionHigh correlation
body_type is highly correlated with maximum_seatingHigh correlation
transmission is highly correlated with transmission_displayHigh correlation
back_legroom is highly correlated with body_type and 8 other fieldsHigh correlation
body_type is highly correlated with back_legroom and 12 other fieldsHigh correlation
city_fuel_economy is highly correlated with engine_cylinders and 11 other fieldsHigh correlation
daysonmarket is highly correlated with listing_idHigh correlation
engine_cylinders is highly correlated with body_type and 16 other fieldsHigh correlation
engine_displacement is highly correlated with body_type and 14 other fieldsHigh correlation
franchise_make is highly correlated with back_legroom and 17 other fieldsHigh correlation
front_legroom is highly correlated with franchise_makeHigh correlation
fuel_type is highly correlated with engine_cylindersHigh correlation
height is highly correlated with back_legroom and 13 other fieldsHigh correlation
highway_fuel_economy is highly correlated with body_type and 12 other fieldsHigh correlation
horsepower is highly correlated with back_legroom and 14 other fieldsHigh correlation
is_new is highly correlated with yearHigh correlation
length is highly correlated with back_legroom and 14 other fieldsHigh correlation
listing_id is highly correlated with daysonmarketHigh correlation
maximum_seating is highly correlated with back_legroom and 11 other fieldsHigh correlation
transmission is highly correlated with engine_cylinders and 2 other fieldsHigh correlation
transmission_display is highly correlated with city_fuel_economy and 10 other fieldsHigh correlation
wheel_system is highly correlated with body_type and 13 other fieldsHigh correlation
wheelbase is highly correlated with back_legroom and 14 other fieldsHigh correlation
width is highly correlated with back_legroom and 15 other fieldsHigh correlation
year is highly correlated with is_newHigh correlation
Torque_lb_ft is highly correlated with back_legroom and 15 other fieldsHigh correlation
Torque_RPM is highly correlated with engine_cylinders and 5 other fieldsHigh correlation
listing_id has unique values Unique
mileage has 97317 (8.8%) zeros Zeros

Reproduction

Analysis started2022-09-04 20:06:59.888502
Analysis finished2022-09-04 20:12:11.664750
Duration5 minutes and 11.78 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

back_legroom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.16277464
Minimum0
Maximum59.8
Zeros183
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:11.835238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.4
Q136.1
median38.3
Q339.9
95-th percentile43.6
Maximum59.8
Range59.8
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation3.009839003
Coefficient of variation (CV)0.07886845315
Kurtosis4.943097315
Mean38.16277464
Median Absolute Deviation (MAD)1.8
Skewness-0.4878789797
Sum41979052.1
Variance9.059130826
MonotonicityNot monotonic
2022-09-05T01:42:11.993523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.347851
 
4.4%
43.647609
 
4.3%
35.741227
 
3.7%
40.432427
 
2.9%
38.631567
 
2.9%
37.431181
 
2.8%
37.929463
 
2.7%
35.228621
 
2.6%
38.426776
 
2.4%
38.125858
 
2.4%
Other values (175)757420
68.9%
ValueCountFrequency (%)
0183
< 0.1%
3.52
 
< 0.1%
17.13
 
< 0.1%
20.23
 
< 0.1%
23.126
 
< 0.1%
23.71
 
< 0.1%
23.81
 
< 0.1%
24.6415
< 0.1%
24.8145
 
< 0.1%
24.95
 
< 0.1%
ValueCountFrequency (%)
59.82
 
< 0.1%
491
 
< 0.1%
47.566
 
< 0.1%
47.416
 
< 0.1%
46.843
 
< 0.1%
45.222022
2.0%
44.5108
 
< 0.1%
44.4687
 
0.1%
44.32333
 
0.2%
44.27
 
< 0.1%

body_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
SUV / Crossover
594453 
Sedan
253830 
Pickup Truck
158090 
Minivan
 
36023
Coupe
 
21119
Other values (4)
 
36485

Length

Max length15
Median length15
Mean length11.56969818
Min length3

Characters and Unicode

Total characters12726668
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSedan
2nd rowCoupe
3rd rowSUV / Crossover
4th rowSUV / Crossover
5th rowSUV / Crossover

Common Values

ValueCountFrequency (%)
SUV / Crossover594453
54.0%
Sedan253830
23.1%
Pickup Truck158090
 
14.4%
Minivan36023
 
3.3%
Coupe21119
 
1.9%
Hatchback18856
 
1.7%
Wagon11888
 
1.1%
Convertible4940
 
0.4%
Van801
 
0.1%

Length

2022-09-05T01:42:12.228535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T01:42:12.446989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
suv594453
24.3%
594453
24.3%
crossover594453
24.3%
sedan253830
10.4%
pickup158090
 
6.5%
truck158090
 
6.5%
minivan36023
 
1.5%
coupe21119
 
0.9%
hatchback18856
 
0.8%
wagon11888
 
0.5%
Other values (2)5741
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r1351936
 
10.6%
1346996
 
10.6%
o1226853
 
9.6%
s1188906
 
9.3%
e879282
 
6.9%
S848283
 
6.7%
v635416
 
5.0%
C620512
 
4.9%
V595254
 
4.7%
/594453
 
4.7%
Other values (19)3438777
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7743770
60.8%
Uppercase Letter3041449
 
23.9%
Space Separator1346996
 
10.6%
Other Punctuation594453
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1351936
17.5%
o1226853
15.8%
s1188906
15.4%
e879282
11.4%
v635416
8.2%
c353892
 
4.6%
n343505
 
4.4%
a340254
 
4.4%
u337299
 
4.4%
k335036
 
4.3%
Other values (8)751391
9.7%
Uppercase Letter
ValueCountFrequency (%)
S848283
27.9%
C620512
20.4%
V595254
19.6%
U594453
19.5%
T158090
 
5.2%
P158090
 
5.2%
M36023
 
1.2%
H18856
 
0.6%
W11888
 
0.4%
Space Separator
ValueCountFrequency (%)
1346996
100.0%
Other Punctuation
ValueCountFrequency (%)
/594453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10785219
84.7%
Common1941449
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1351936
12.5%
o1226853
11.4%
s1188906
11.0%
e879282
 
8.2%
S848283
 
7.9%
v635416
 
5.9%
C620512
 
5.8%
V595254
 
5.5%
U594453
 
5.5%
c353892
 
3.3%
Other values (17)2490432
23.1%
Common
ValueCountFrequency (%)
1346996
69.4%
/594453
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII12726668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1351936
 
10.6%
1346996
 
10.6%
o1226853
 
9.6%
s1188906
 
9.3%
e879282
 
6.9%
S848283
 
6.7%
v635416
 
5.0%
C620512
 
4.9%
V595254
 
4.7%
/594453
 
4.7%
Other values (19)3438777
27.0%

city_fuel_economy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.46764727
Minimum9
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:12.603679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15
Q118
median21
Q325
95-th percentile30
Maximum74
Range65
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.669318466
Coefficient of variation (CV)0.2175048997
Kurtosis0.5862573737
Mean21.46764727
Median Absolute Deviation (MAD)4
Skewness0.4908857145
Sum23614412
Variance21.80253494
MonotonicityNot monotonic
2022-09-05T01:42:12.757464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1998472
 
9.0%
1895954
 
8.7%
2286645
 
7.9%
2178977
 
7.2%
1678296
 
7.1%
1774296
 
6.8%
2672047
 
6.5%
2071110
 
6.5%
1567130
 
6.1%
2560729
 
5.5%
Other values (39)316344
28.8%
ValueCountFrequency (%)
930
 
< 0.1%
1037
 
< 0.1%
11269
 
< 0.1%
124014
 
0.4%
138587
 
0.8%
1415674
 
1.4%
1567130
6.1%
1678296
7.1%
1774296
6.8%
1895954
8.7%
ValueCountFrequency (%)
744
 
< 0.1%
7019
 
< 0.1%
6750
< 0.1%
6628
< 0.1%
651
 
< 0.1%
636
 
< 0.1%
627
 
< 0.1%
601
 
< 0.1%
5912
 
< 0.1%
5829
< 0.1%

daysonmarket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1225
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.98140636
Minimum0
Maximum2567
Zeros10983
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:12.933428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q118
median40
Q389
95-th percentile293
Maximum2567
Range2567
Interquartile range (IQR)71

Descriptive statistics

Standard deviation103.930005
Coefficient of variation (CV)1.299427076
Kurtosis21.74913493
Mean79.98140636
Median Absolute Deviation (MAD)27
Skewness3.118205729
Sum87979547
Variance10801.44594
MonotonicityNot monotonic
2022-09-05T01:42:13.083451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
823286
 
2.1%
1320785
 
1.9%
1420626
 
1.9%
1520340
 
1.8%
1220246
 
1.8%
719631
 
1.8%
2019567
 
1.8%
618869
 
1.7%
2118320
 
1.7%
1917753
 
1.6%
Other values (1215)900577
81.9%
ValueCountFrequency (%)
010983
1.0%
112234
1.1%
28402
 
0.8%
36220
 
0.6%
410979
1.0%
516832
1.5%
618869
1.7%
719631
1.8%
823286
2.1%
916440
1.5%
ValueCountFrequency (%)
25671
< 0.1%
25271
< 0.1%
24991
< 0.1%
24641
< 0.1%
23371
< 0.1%
22961
< 0.1%
21941
< 0.1%
21821
< 0.1%
21501
< 0.1%
20731
< 0.1%

engine_cylinders
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
I4
567169 
V6
320651 
V8
103424 
H4
 
27590
V6 Flex Fuel Vehicle
 
25969
Other values (21)
 
55197

Length

Max length25
Median length2
Mean length2.833386364
Min length2

Characters and Unicode

Total characters3116725
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowI4
2nd rowV6
3rd rowV6
4th rowV6
5th rowV6

Common Values

ValueCountFrequency (%)
I4567169
51.6%
V6320651
29.2%
V8103424
 
9.4%
H427590
 
2.5%
V6 Flex Fuel Vehicle25969
 
2.4%
V8 Flex Fuel Vehicle18931
 
1.7%
I316448
 
1.5%
I67473
 
0.7%
I6 Diesel2930
 
0.3%
I4 Flex Fuel Vehicle2474
 
0.2%
Other values (16)6941
 
0.6%

Length

2022-09-05T01:42:13.204343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i4571729
45.7%
v6349694
28.0%
v8122355
 
9.8%
flex47387
 
3.8%
fuel47387
 
3.8%
vehicle47387
 
3.8%
h427628
 
2.2%
i316448
 
1.3%
i610407
 
0.8%
diesel5540
 
0.4%
Other values (11)4337
 
0.3%

Most occurring characters

ValueCountFrequency (%)
I599361
19.2%
4599357
19.2%
V519606
16.7%
6360743
11.6%
e204948
 
6.6%
150299
 
4.8%
l149861
 
4.8%
8122355
 
3.9%
F94774
 
3.0%
i57673
 
1.9%
Other values (27)257748
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1250299
40.1%
Decimal Number1100316
35.3%
Lowercase Letter615811
19.8%
Space Separator150299
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e204948
33.3%
l149861
24.3%
i57673
 
9.4%
u47390
 
7.7%
c47387
 
7.7%
x47387
 
7.7%
h47387
 
7.7%
s7706
 
1.3%
d2592
 
0.4%
o2160
 
0.4%
Other values (7)1320
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
I599361
47.9%
V519606
41.6%
F94774
 
7.6%
H28702
 
2.3%
D5540
 
0.4%
B2157
 
0.2%
W146
 
< 0.1%
R4
 
< 0.1%
C3
 
< 0.1%
N3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4599357
54.5%
6360743
32.8%
8122355
 
11.1%
316448
 
1.5%
5777
 
0.1%
1316
 
< 0.1%
2316
 
< 0.1%
04
 
< 0.1%
Space Separator
ValueCountFrequency (%)
150299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1866110
59.9%
Common1250615
40.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I599361
32.1%
V519606
27.8%
e204948
 
11.0%
l149861
 
8.0%
F94774
 
5.1%
i57673
 
3.1%
u47390
 
2.5%
c47387
 
2.5%
x47387
 
2.5%
h47387
 
2.5%
Other values (18)50336
 
2.7%
Common
ValueCountFrequency (%)
4599357
47.9%
6360743
28.8%
150299
 
12.0%
8122355
 
9.8%
316448
 
1.3%
5777
 
0.1%
1316
 
< 0.1%
2316
 
< 0.1%
04
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3116725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I599361
19.2%
4599357
19.2%
V519606
16.7%
6360743
11.6%
e204948
 
6.6%
150299
 
4.8%
l149861
 
4.8%
8122355
 
3.9%
F94774
 
3.0%
i57673
 
1.9%
Other values (27)257748
8.3%

engine_displacement
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2890.219091
Minimum1000
Maximum8100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:13.334490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1400
Q12000
median2500
Q33500
95-th percentile5700
Maximum8100
Range7100
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation1210.581976
Coefficient of variation (CV)0.4188547435
Kurtosis0.4886084598
Mean2890.219091
Median Absolute Deviation (MAD)1000
Skewness0.9929029442
Sum3179241000
Variance1465508.721
MonotonicityNot monotonic
2022-09-05T01:42:13.464474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000222852
20.3%
3500154296
14.0%
3600136966
12.5%
2500119564
10.9%
240085571
 
7.8%
150066315
 
6.0%
140041781
 
3.8%
570037111
 
3.4%
300033245
 
3.0%
530026198
 
2.4%
Other values (46)176101
16.0%
ValueCountFrequency (%)
10004364
 
0.4%
12004459
 
0.4%
130015116
 
1.4%
140041781
 
3.8%
150066315
 
6.0%
160023911
 
2.2%
170034
 
< 0.1%
180023036
 
2.1%
190015
 
< 0.1%
2000222852
20.3%
ValueCountFrequency (%)
81001
 
< 0.1%
680019
 
< 0.1%
670012
 
< 0.1%
6600105
 
< 0.1%
64002349
 
0.2%
630022
 
< 0.1%
620023463
2.1%
610059
 
< 0.1%
6000439
 
< 0.1%
590014
 
< 0.1%

franchise_make
Categorical

HIGH CORRELATION

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
Chevrolet
161324 
Ford
153403 
Jeep
99068 
Honda
94234 
Nissan
82237 
Other values (39)
509734 

Length

Max length13
Median length11
Mean length5.858822727
Min length3

Characters and Unicode

Total characters6444705
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChevrolet
2nd rowJeep
3rd rowChevrolet
4th rowChevrolet
5th rowChevrolet

Common Values

ValueCountFrequency (%)
Chevrolet161324
14.7%
Ford153403
13.9%
Jeep99068
 
9.0%
Honda94234
 
8.6%
Nissan82237
 
7.5%
Toyota57253
 
5.2%
Hyundai48561
 
4.4%
Kia41656
 
3.8%
Buick35958
 
3.3%
Dodge29933
 
2.7%
Other values (34)296373
26.9%

Length

2022-09-05T01:42:13.594718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chevrolet161324
14.6%
ford153403
13.9%
jeep99068
 
8.9%
honda94234
 
8.5%
nissan82237
 
7.4%
toyota57253
 
5.2%
hyundai48561
 
4.4%
kia41656
 
3.8%
buick35958
 
3.2%
dodge29933
 
2.7%
Other values (37)303820
27.4%

Most occurring characters

ValueCountFrequency (%)
e684249
 
10.6%
o626561
 
9.7%
a491996
 
7.6%
r412297
 
6.4%
d404855
 
6.3%
n305450
 
4.7%
i285099
 
4.4%
l268411
 
4.2%
s253503
 
3.9%
t229119
 
3.6%
Other values (35)2483165
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5073426
78.7%
Uppercase Letter1345301
 
20.9%
Dash Punctuation18531
 
0.3%
Space Separator7447
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e684249
13.5%
o626561
12.3%
a491996
9.7%
r412297
 
8.1%
d404855
 
8.0%
n305450
 
6.0%
i285099
 
5.6%
l268411
 
5.3%
s253503
 
5.0%
t229119
 
4.5%
Other values (14)1111886
21.9%
Uppercase Letter
ValueCountFrequency (%)
C220054
16.4%
F164500
12.2%
H142795
10.6%
M122782
9.1%
N103588
7.7%
J101471
7.5%
B72306
 
5.4%
T68349
 
5.1%
A54181
 
4.0%
I44180
 
3.3%
Other values (9)251095
18.7%
Dash Punctuation
ValueCountFrequency (%)
-18531
100.0%
Space Separator
ValueCountFrequency (%)
7447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6418727
99.6%
Common25978
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e684249
 
10.7%
o626561
 
9.8%
a491996
 
7.7%
r412297
 
6.4%
d404855
 
6.3%
n305450
 
4.8%
i285099
 
4.4%
l268411
 
4.2%
s253503
 
3.9%
t229119
 
3.6%
Other values (33)2457187
38.3%
Common
ValueCountFrequency (%)
-18531
71.3%
7447
28.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6444705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e684249
 
10.6%
o626561
 
9.7%
a491996
 
7.6%
r412297
 
6.4%
d404855
 
6.3%
n305450
 
4.7%
i285099
 
4.4%
l268411
 
4.2%
s253503
 
3.9%
t229119
 
3.6%
Other values (35)2483165
38.5%

front_legroom
Real number (ℝ≥0)

HIGH CORRELATION

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.17207409
Minimum0
Maximum52.5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:13.718498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.3
Q141
median41.8
Q343
95-th percentile45
Maximum52.5
Range52.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.451929618
Coefficient of variation (CV)0.03442869835
Kurtosis0.622369386
Mean42.17207409
Median Absolute Deviation (MAD)0.9
Skewness0.6302724953
Sum46389281.5
Variance2.108099615
MonotonicityNot monotonic
2022-09-05T01:42:13.855856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.990320
 
8.2%
43.966621
 
6.1%
41.263529
 
5.8%
4158899
 
5.4%
41.347265
 
4.3%
42.346994
 
4.3%
40.845211
 
4.1%
41.143291
 
3.9%
44.537594
 
3.4%
40.335994
 
3.3%
Other values (68)564282
51.3%
ValueCountFrequency (%)
01
 
< 0.1%
35.8100
 
< 0.1%
36.61001
0.1%
37.7304
 
< 0.1%
37.81
 
< 0.1%
388
 
< 0.1%
38.759
 
< 0.1%
38.942
 
< 0.1%
39483
 
< 0.1%
39.11410
0.1%
ValueCountFrequency (%)
52.5129
 
< 0.1%
46.991
 
< 0.1%
46.4546
 
< 0.1%
46.3148
 
< 0.1%
46.14151
 
0.4%
45.925
 
< 0.1%
45.83509
 
0.3%
45.71819
 
0.2%
45.511020
1.0%
45.4351
 
< 0.1%

fuel_tank_volume
Categorical

HIGH CARDINALITY

Distinct139
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
26 gal
96441 
14 gal
 
65603
18.5 gal
 
63475
13.2 gal
 
56416
15.9 gal
 
45064
Other values (134)
773001 

Length

Max length8
Median length8
Mean length7.343633636
Min length5

Characters and Unicode

Total characters8077997
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row15.8 gal
2nd row17.4 gal
3rd row19.4 gal
4th row22 gal
5th row24.6 gal

Common Values

ValueCountFrequency (%)
26 gal96441
 
8.8%
14 gal65603
 
6.0%
18.5 gal63475
 
5.8%
13.2 gal56416
 
5.1%
15.9 gal45064
 
4.1%
14.5 gal39962
 
3.6%
19 gal38990
 
3.5%
19.5 gal37188
 
3.4%
24 gal30311
 
2.8%
24.6 gal29827
 
2.7%
Other values (129)596723
54.2%

Length

2022-09-05T01:42:13.991519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gal1100000
50.0%
2696441
 
4.4%
1465603
 
3.0%
18.563475
 
2.9%
13.256416
 
2.6%
15.945064
 
2.0%
14.539962
 
1.8%
1938990
 
1.8%
19.537188
 
1.7%
2430311
 
1.4%
Other values (130)626550
28.5%

Most occurring characters

ValueCountFrequency (%)
1100000
13.6%
g1100000
13.6%
a1100000
13.6%
l1100000
13.6%
1865792
10.7%
.740083
9.2%
2485851
6.0%
5318544
 
3.9%
4295196
 
3.7%
6279146
 
3.5%
Other values (5)693385
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3300000
40.9%
Decimal Number2937914
36.4%
Space Separator1100000
 
13.6%
Other Punctuation740083
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1865792
29.5%
2485851
16.5%
5318544
 
10.8%
4295196
 
10.0%
6279146
 
9.5%
8209630
 
7.1%
9184541
 
6.3%
3157882
 
5.4%
795488
 
3.3%
045844
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
g1100000
33.3%
a1100000
33.3%
l1100000
33.3%
Space Separator
ValueCountFrequency (%)
1100000
100.0%
Other Punctuation
ValueCountFrequency (%)
.740083
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4777997
59.1%
Latin3300000
40.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1100000
23.0%
1865792
18.1%
.740083
15.5%
2485851
10.2%
5318544
 
6.7%
4295196
 
6.2%
6279146
 
5.8%
8209630
 
4.4%
9184541
 
3.9%
3157882
 
3.3%
Other values (2)141332
 
3.0%
Latin
ValueCountFrequency (%)
g1100000
33.3%
a1100000
33.3%
l1100000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8077997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1100000
13.6%
g1100000
13.6%
a1100000
13.6%
l1100000
13.6%
1865792
10.7%
.740083
9.2%
2485851
6.0%
5318544
 
3.9%
4295196
 
3.7%
6279146
 
3.5%
Other values (5)693385
8.6%

fuel_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
Gasoline
1044481 
Flex Fuel Vehicle
 
47387
Diesel
 
5540
Biodiesel
 
2157
Hybrid
 
432

Length

Max length22
Median length8
Mean length8.378852727
Min length6

Characters and Unicode

Total characters9216738
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGasoline
2nd rowGasoline
3rd rowGasoline
4th rowGasoline
5th rowGasoline

Common Values

ValueCountFrequency (%)
Gasoline1044481
95.0%
Flex Fuel Vehicle47387
 
4.3%
Diesel5540
 
0.5%
Biodiesel2157
 
0.2%
Hybrid432
 
< 0.1%
Compressed Natural Gas3
 
< 0.1%

Length

2022-09-05T01:42:14.108515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T01:42:14.235676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
gasoline1044481
87.4%
flex47387
 
4.0%
fuel47387
 
4.0%
vehicle47387
 
4.0%
diesel5540
 
0.5%
biodiesel2157
 
0.2%
hybrid432
 
< 0.1%
compressed3
 
< 0.1%
natural3
 
< 0.1%
gas3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e1249429
13.6%
l1194342
13.0%
i1102154
12.0%
s1052187
11.4%
o1046641
11.4%
a1044490
11.3%
G1044484
11.3%
n1044481
11.3%
94780
 
1.0%
F94774
 
1.0%
Other values (17)248976
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7927178
86.0%
Uppercase Letter1194780
 
13.0%
Space Separator94780
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1249429
15.8%
l1194342
15.1%
i1102154
13.9%
s1052187
13.3%
o1046641
13.2%
a1044490
13.2%
n1044481
13.2%
u47390
 
0.6%
c47387
 
0.6%
h47387
 
0.6%
Other values (8)51290
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
G1044484
87.4%
F94774
 
7.9%
V47387
 
4.0%
D5540
 
0.5%
B2157
 
0.2%
H432
 
< 0.1%
C3
 
< 0.1%
N3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
94780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9121958
99.0%
Common94780
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1249429
13.7%
l1194342
13.1%
i1102154
12.1%
s1052187
11.5%
o1046641
11.5%
a1044490
11.5%
G1044484
11.5%
n1044481
11.5%
F94774
 
1.0%
u47390
 
0.5%
Other values (16)201586
 
2.2%
Common
ValueCountFrequency (%)
94780
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9216738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1249429
13.6%
l1194342
13.0%
i1102154
12.0%
s1052187
11.4%
o1046641
11.4%
a1044490
11.3%
G1044484
11.3%
n1044481
11.3%
94780
 
1.0%
F94774
 
1.0%
Other values (17)248976
 
2.7%

height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct295
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.93010982
Minimum49
Maximum108.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:14.398376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile56.5
Q158.9
median66.2
Q369.9
95-th percentile77.2
Maximum108.6
Range59.6
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.575773861
Coefficient of variation (CV)0.09973855465
Kurtosis-0.7648451871
Mean65.93010982
Median Absolute Deviation (MAD)4.4
Skewness0.08986854105
Sum72523120.8
Variance43.24080187
MonotonicityNot monotonic
2022-09-05T01:42:14.526479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.338857
 
3.5%
77.236280
 
3.3%
66.133361
 
3.0%
66.531425
 
2.9%
69.926218
 
2.4%
57.124815
 
2.3%
64.824371
 
2.2%
56.522806
 
2.1%
58.122299
 
2.0%
69.321716
 
2.0%
Other values (285)817852
74.4%
ValueCountFrequency (%)
491
 
< 0.1%
49.11
 
< 0.1%
49.71
 
< 0.1%
505
 
< 0.1%
50.21
 
< 0.1%
50.41
 
< 0.1%
50.659
< 0.1%
50.81
 
< 0.1%
512
 
< 0.1%
51.27
 
< 0.1%
ValueCountFrequency (%)
108.615
 
< 0.1%
107.716
 
< 0.1%
99.2149
< 0.1%
99.12
 
< 0.1%
98.718
 
< 0.1%
8811
 
< 0.1%
84.617
 
< 0.1%
84.52
 
< 0.1%
84.117
 
< 0.1%
83.94
 
< 0.1%

highway_fuel_economy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.57625182
Minimum11
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:14.678585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21
Q125
median28
Q332
95-th percentile39
Maximum75
Range64
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.403067935
Coefficient of variation (CV)0.1890754592
Kurtosis-0.1370074985
Mean28.57625182
Median Absolute Deviation (MAD)4
Skewness0.3622593934
Sum31433877
Variance29.19314311
MonotonicityNot monotonic
2022-09-05T01:42:14.808752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2583402
 
7.6%
2278261
 
7.1%
3077862
 
7.1%
2876272
 
6.9%
2973550
 
6.7%
2771695
 
6.5%
2668184
 
6.2%
3167226
 
6.1%
3257626
 
5.2%
2154709
 
5.0%
Other values (40)391213
35.6%
ValueCountFrequency (%)
1110
 
< 0.1%
1214
 
< 0.1%
1338
 
< 0.1%
1487
 
< 0.1%
15157
 
< 0.1%
16435
 
< 0.1%
173751
 
0.3%
188076
0.7%
199729
0.9%
2016803
1.5%
ValueCountFrequency (%)
7511
 
< 0.1%
744
 
< 0.1%
7050
< 0.1%
6844
< 0.1%
647
 
< 0.1%
616
 
< 0.1%
581
 
< 0.1%
5769
< 0.1%
552
 
< 0.1%
5321
 
< 0.1%

horsepower
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct342
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.7586518
Minimum74
Maximum808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:14.976134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile138
Q1175
median248
Q3300
95-th percentile395
Maximum808
Range734
Interquartile range (IQR)125

Descriptive statistics

Standard deviation86.48017249
Coefficient of variation (CV)0.3490500609
Kurtosis0.09316822693
Mean247.7586518
Median Absolute Deviation (MAD)66
Skewness0.5975287334
Sum272534517
Variance7478.820235
MonotonicityNot monotonic
2022-09-05T01:42:15.113380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37546772
 
4.3%
17046707
 
4.2%
39540503
 
3.7%
31036740
 
3.3%
29535131
 
3.2%
13833068
 
3.0%
14731199
 
2.8%
35525104
 
2.3%
28025014
 
2.3%
18024816
 
2.3%
Other values (332)754946
68.6%
ValueCountFrequency (%)
7449
 
< 0.1%
781939
0.2%
84179
 
< 0.1%
901
 
< 0.1%
934
 
< 0.1%
961
 
< 0.1%
10045
 
< 0.1%
101167
 
< 0.1%
10312
 
< 0.1%
1044
 
< 0.1%
ValueCountFrequency (%)
80813
 
< 0.1%
797125
 
< 0.1%
76037
 
< 0.1%
717127
 
< 0.1%
707396
< 0.1%
7004
 
< 0.1%
66215
 
< 0.1%
650135
 
< 0.1%
64016
 
< 0.1%
6332
 
< 0.1%

is_new
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
True
572196 
False
527804 
ValueCountFrequency (%)
True572196
52.0%
False527804
48.0%
2022-09-05T01:42:15.253372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct597
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.8080766
Minimum139.6
Maximum266.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:15.373251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum139.6
5-th percentile167.6
Q1182
median189.8
Q3200.6
95-th percentile231.9
Maximum266.1
Range126.5
Interquartile range (IQR)18.6

Descriptive statistics

Standard deviation18.65402679
Coefficient of variation (CV)0.09674919805
Kurtosis0.1563793121
Mean192.8080766
Median Absolute Deviation (MAD)8.7
Skewness0.8093609631
Sum212088884.3
Variance347.9727156
MonotonicityNot monotonic
2022-09-05T01:42:15.515612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231.951431
 
4.7%
184.528921
 
2.6%
231.727359
 
2.5%
167.623986
 
2.2%
189.821457
 
2.0%
232.920903
 
1.9%
182.120567
 
1.9%
204.320539
 
1.9%
183.120507
 
1.9%
18219447
 
1.8%
Other values (587)844883
76.8%
ValueCountFrequency (%)
139.6173
 
< 0.1%
142.82
 
< 0.1%
143.12
 
< 0.1%
143.99
 
< 0.1%
144.465
 
< 0.1%
144.7179
 
< 0.1%
145.665
 
< 0.1%
146.256
 
< 0.1%
148.849
 
< 0.1%
149.41059
0.1%
ValueCountFrequency (%)
266.12
 
< 0.1%
263.916
 
< 0.1%
250.521
 
< 0.1%
250.46
 
< 0.1%
250.33
 
< 0.1%
249.210
 
< 0.1%
2494
 
< 0.1%
247.97
 
< 0.1%
247.856
< 0.1%
247.62
 
< 0.1%

listed_date
Categorical

HIGH CARDINALITY

Distinct1212
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2020-09-02
 
24503
2020-08-26
 
21314
2020-09-03
 
21300
2020-08-28
 
21160
2020-08-29
 
20748
Other values (1207)
990975 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters11000000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)< 0.1%

Sample

1st row2020-07-16
2nd row2020-08-04
3rd row2020-08-13
4th row2020-08-13
5th row2020-08-16

Common Values

ValueCountFrequency (%)
2020-09-0224503
 
2.2%
2020-08-2621314
 
1.9%
2020-09-0321300
 
1.9%
2020-08-2821160
 
1.9%
2020-08-2920748
 
1.9%
2020-08-2720581
 
1.9%
2020-08-2120020
 
1.8%
2020-08-2218923
 
1.7%
2020-09-0418218
 
1.7%
2020-09-0518215
 
1.7%
Other values (1202)895018
81.4%

Length

2022-09-05T01:42:15.665743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-09-0224503
 
2.2%
2020-08-2621314
 
1.9%
2020-09-0321300
 
1.9%
2020-08-2821160
 
1.9%
2020-08-2920748
 
1.9%
2020-08-2720581
 
1.9%
2020-08-2120020
 
1.8%
2020-08-2218923
 
1.7%
2020-09-0418218
 
1.7%
2020-09-0518215
 
1.7%
Other values (1202)895018
81.4%

Most occurring characters

ValueCountFrequency (%)
03636027
33.1%
22667673
24.3%
-2200000
20.0%
1634755
 
5.8%
8527848
 
4.8%
9355762
 
3.2%
7305494
 
2.8%
3213391
 
1.9%
6195174
 
1.8%
5140293
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8800000
80.0%
Dash Punctuation2200000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03636027
41.3%
22667673
30.3%
1634755
 
7.2%
8527848
 
6.0%
9355762
 
4.0%
7305494
 
3.5%
3213391
 
2.4%
6195174
 
2.2%
5140293
 
1.6%
4123583
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-2200000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03636027
33.1%
22667673
24.3%
-2200000
20.0%
1634755
 
5.8%
8527848
 
4.8%
9355762
 
3.2%
7305494
 
2.8%
3213391
 
1.9%
6195174
 
1.8%
5140293
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03636027
33.1%
22667673
24.3%
-2200000
20.0%
1634755
 
5.8%
8527848
 
4.8%
9355762
 
3.2%
7305494
 
2.8%
3213391
 
1.9%
6195174
 
1.8%
5140293
 
1.3%

listing_color
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
BLACK
228733 
WHITE
219585 
UNKNOWN
157496 
SILVER
141920 
GRAY
138705 
Other values (10)
213561 

Length

Max length7
Median length6
Mean length5.028751818
Min length3

Characters and Unicode

Total characters5531627
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSILVER
2nd rowBLACK
3rd rowSILVER
4th rowSILVER
5th rowBLACK

Common Values

ValueCountFrequency (%)
BLACK228733
20.8%
WHITE219585
20.0%
UNKNOWN157496
14.3%
SILVER141920
12.9%
GRAY138705
12.6%
RED97584
8.9%
BLUE94344
8.6%
GREEN7175
 
0.7%
BROWN5132
 
0.5%
ORANGE4416
 
0.4%
Other values (5)4910
 
0.4%

Length

2022-09-05T01:42:15.866661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black228733
20.8%
white219585
20.0%
unknown157496
14.3%
silver141920
12.9%
gray138705
12.6%
red97584
8.9%
blue94344
8.6%
green7175
 
0.7%
brown5132
 
0.5%
orange4416
 
0.4%
Other values (5)4910
 
0.4%

Most occurring characters

ValueCountFrequency (%)
E575480
 
10.4%
N489241
 
8.8%
L471221
 
8.5%
R395301
 
7.1%
K386259
 
7.0%
W383557
 
6.9%
A373422
 
6.8%
I361535
 
6.5%
B328209
 
5.9%
U252209
 
4.6%
Other values (10)1515193
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5531627
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E575480
 
10.4%
N489241
 
8.8%
L471221
 
8.5%
R395301
 
7.1%
K386259
 
7.0%
W383557
 
6.9%
A373422
 
6.8%
I361535
 
6.5%
B328209
 
5.9%
U252209
 
4.6%
Other values (10)1515193
27.4%

Most occurring scripts

ValueCountFrequency (%)
Latin5531627
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E575480
 
10.4%
N489241
 
8.8%
L471221
 
8.5%
R395301
 
7.1%
K386259
 
7.0%
W383557
 
6.9%
A373422
 
6.8%
I361535
 
6.5%
B328209
 
5.9%
U252209
 
4.6%
Other values (10)1515193
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5531627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E575480
 
10.4%
N489241
 
8.8%
L471221
 
8.5%
R395301
 
7.1%
K386259
 
7.0%
W383557
 
6.9%
A373422
 
6.8%
I361535
 
6.5%
B328209
 
5.9%
U252209
 
4.6%
Other values (10)1515193
27.4%

listing_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275174353.3
Minimum67762256
Maximum281871240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:16.108338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum67762256
5-th percentile258643465.6
Q1274018498.5
median278172000
Q3280140858.8
95-th percentile281419600.2
Maximum281871240
Range214108984
Interquartile range (IQR)6122360.25

Descriptive statistics

Standard deviation8378872.014
Coefficient of variation (CV)0.03044932026
Kurtosis29.13908752
Mean275174353.3
Median Absolute Deviation (MAD)2424139.5
Skewness-3.598259209
Sum3.026917887 × 1014
Variance7.020549622 × 1013
MonotonicityNot monotonic
2022-09-05T01:42:16.263777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2766753041
 
< 0.1%
2776870971
 
< 0.1%
2589691821
 
< 0.1%
2789089461
 
< 0.1%
2786097571
 
< 0.1%
2812868671
 
< 0.1%
2777411271
 
< 0.1%
2734787631
 
< 0.1%
2761765011
 
< 0.1%
2612672171
 
< 0.1%
Other values (1099990)1099990
> 99.9%
ValueCountFrequency (%)
677622561
< 0.1%
707354321
< 0.1%
726760561
< 0.1%
752576311
< 0.1%
839387761
< 0.1%
869015291
< 0.1%
953572131
< 0.1%
963050131
< 0.1%
987306251
< 0.1%
1043514841
< 0.1%
ValueCountFrequency (%)
2818712401
< 0.1%
2818712271
< 0.1%
2818712251
< 0.1%
2818700181
< 0.1%
2818700051
< 0.1%
2818685711
< 0.1%
2818685691
< 0.1%
2818685681
< 0.1%
2818685661
< 0.1%
2818668671
< 0.1%

maximum_seating
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
5 seats
742593 
7 seats
125754 
6 seats
119943 
8 seats
87657 
4 seats
 
22713
Other values (6)
 
1340

Length

Max length8
Median length7
Mean length7.000467273
Min length7

Characters and Unicode

Total characters7700514
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5 seats
2nd row4 seats
3rd row8 seats
4th row8 seats
5th row5 seats

Common Values

ValueCountFrequency (%)
5 seats742593
67.5%
7 seats125754
 
11.4%
6 seats119943
 
10.9%
8 seats87657
 
8.0%
4 seats22713
 
2.1%
9 seats640
 
0.1%
15 seats453
 
< 0.1%
3 seats181
 
< 0.1%
10 seats35
 
< 0.1%
12 seats26
 
< 0.1%

Length

2022-09-05T01:42:16.388550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seats1100000
50.0%
5742593
33.8%
7125754
 
5.7%
6119943
 
5.5%
887657
 
4.0%
422713
 
1.0%
9640
 
< 0.1%
15453
 
< 0.1%
3181
 
< 0.1%
1035
 
< 0.1%
Other values (2)31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s2200000
28.6%
1100000
14.3%
e1100000
14.3%
a1100000
14.3%
t1100000
14.3%
5743046
 
9.6%
7125754
 
1.6%
6119943
 
1.6%
887657
 
1.1%
422713
 
0.3%
Other values (5)1401
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5500000
71.4%
Decimal Number1100514
 
14.3%
Space Separator1100000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5743046
67.5%
7125754
 
11.4%
6119943
 
10.9%
887657
 
8.0%
422713
 
2.1%
9640
 
0.1%
1514
 
< 0.1%
3181
 
< 0.1%
035
 
< 0.1%
231
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
s2200000
40.0%
e1100000
20.0%
a1100000
20.0%
t1100000
20.0%
Space Separator
ValueCountFrequency (%)
1100000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5500000
71.4%
Common2200514
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1100000
50.0%
5743046
33.8%
7125754
 
5.7%
6119943
 
5.5%
887657
 
4.0%
422713
 
1.0%
9640
 
< 0.1%
1514
 
< 0.1%
3181
 
< 0.1%
035
 
< 0.1%
Latin
ValueCountFrequency (%)
s2200000
40.0%
e1100000
20.0%
a1100000
20.0%
t1100000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7700514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s2200000
28.6%
1100000
14.3%
e1100000
14.3%
a1100000
14.3%
t1100000
14.3%
5743046
 
9.6%
7125754
 
1.6%
6119943
 
1.6%
887657
 
1.1%
422713
 
0.3%
Other values (5)1401
 
< 0.1%

mileage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct126812
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21762.19085
Minimum0
Maximum1225238
Zeros97317
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:16.513408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median500
Q333934
95-th percentile93570.05
Maximum1225238
Range1225238
Interquartile range (IQR)33929

Descriptive statistics

Standard deviation33635.16422
Coefficient of variation (CV)1.545578037
Kurtosis12.5673735
Mean21762.19085
Median Absolute Deviation (MAD)500
Skewness2.392922704
Sum2.393840994 × 1010
Variance1131324272
MonotonicityNot monotonic
2022-09-05T01:42:16.659306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
097317
 
8.8%
1061408
 
5.6%
559799
 
5.4%
333176
 
3.0%
231963
 
2.9%
131089
 
2.8%
625852
 
2.4%
1121716
 
2.0%
421661
 
2.0%
1220937
 
1.9%
Other values (126802)695082
63.2%
ValueCountFrequency (%)
097317
8.8%
131089
 
2.8%
231963
 
2.9%
333176
 
3.0%
421661
 
2.0%
559799
5.4%
625852
 
2.4%
719106
 
1.7%
817545
 
1.6%
914771
 
1.3%
ValueCountFrequency (%)
12252381
 
< 0.1%
11111114
< 0.1%
9999991
 
< 0.1%
7857781
 
< 0.1%
5140331
 
< 0.1%
3994961
 
< 0.1%
3815531
 
< 0.1%
3815191
 
< 0.1%
3805841
 
< 0.1%
3796471
 
< 0.1%

model_name
Categorical

HIGH CARDINALITY

Distinct656
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
F-150
 
55407
1500
 
34728
Silverado 1500
 
30136
Rogue
 
27564
Trax
 
24309
Other values (651)
927856 

Length

Max length26
Median length22
Mean length6.696895455
Min length2

Characters and Unicode

Total characters7366585
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st rowMalibu
2nd rowRC 350
3rd rowTraverse
4th rowTraverse
5th rowGrand Cherokee

Common Values

ValueCountFrequency (%)
F-15055407
 
5.0%
150034728
 
3.2%
Silverado 150030136
 
2.7%
Rogue27564
 
2.5%
Trax24309
 
2.2%
CR-V23121
 
2.1%
Grand Cherokee21240
 
1.9%
Cherokee19438
 
1.8%
Altima18151
 
1.7%
Equinox18026
 
1.6%
Other values (646)827880
75.3%

Length

2022-09-05T01:42:16.809996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
150073858
 
5.9%
f-15055407
 
4.4%
cherokee40678
 
3.2%
rogue33023
 
2.6%
grand31856
 
2.5%
silverado30159
 
2.4%
trax24309
 
1.9%
cr-v23121
 
1.8%
civic19165
 
1.5%
altima18215
 
1.5%
Other values (583)903465
72.1%

Most occurring characters

ValueCountFrequency (%)
a647779
 
8.8%
e594312
 
8.1%
r548269
 
7.4%
o458878
 
6.2%
n350955
 
4.8%
i345087
 
4.7%
l253294
 
3.4%
0244423
 
3.3%
t244302
 
3.3%
C228167
 
3.1%
Other values (55)3451119
46.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5006523
68.0%
Uppercase Letter1455651
 
19.8%
Decimal Number623598
 
8.5%
Space Separator153256
 
2.1%
Dash Punctuation125907
 
1.7%
Other Punctuation1650
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a647779
12.9%
e594312
11.9%
r548269
11.0%
o458878
9.2%
n350955
 
7.0%
i345087
 
6.9%
l253294
 
5.1%
t244302
 
4.9%
s222790
 
4.4%
u198698
 
4.0%
Other values (16)1142159
22.8%
Uppercase Letter
ValueCountFrequency (%)
C228167
15.7%
S155928
10.7%
E133861
9.2%
T119260
 
8.2%
R110270
 
7.6%
F105510
 
7.2%
X88146
 
6.1%
A85044
 
5.8%
G65199
 
4.5%
M56785
 
3.9%
Other values (16)307481
21.1%
Decimal Number
ValueCountFrequency (%)
0244423
39.2%
5168908
27.1%
1130634
20.9%
323742
 
3.8%
421206
 
3.4%
618140
 
2.9%
95531
 
0.9%
73991
 
0.6%
83567
 
0.6%
23456
 
0.6%
Space Separator
ValueCountFrequency (%)
153256
100.0%
Dash Punctuation
ValueCountFrequency (%)
-125907
100.0%
Other Punctuation
ValueCountFrequency (%)
&1650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6462174
87.7%
Common904411
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a647779
 
10.0%
e594312
 
9.2%
r548269
 
8.5%
o458878
 
7.1%
n350955
 
5.4%
i345087
 
5.3%
l253294
 
3.9%
t244302
 
3.8%
C228167
 
3.5%
s222790
 
3.4%
Other values (42)2568341
39.7%
Common
ValueCountFrequency (%)
0244423
27.0%
5168908
18.7%
153256
16.9%
1130634
14.4%
-125907
13.9%
323742
 
2.6%
421206
 
2.3%
618140
 
2.0%
95531
 
0.6%
73991
 
0.4%
Other values (3)8673
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7366585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a647779
 
8.8%
e594312
 
8.1%
r548269
 
7.4%
o458878
 
6.2%
n350955
 
4.8%
i345087
 
4.7%
l253294
 
3.4%
0244423
 
3.3%
t244302
 
3.3%
C228167
 
3.1%
Other values (55)3451119
46.8%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct68803
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30329.5275
Minimum484
Maximum2698500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:16.938456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum484
5-th percentile12299
Q119988
median27068
Q338335
95-th percentile56554
Maximum2698500
Range2698016
Interquartile range (IQR)18347

Descriptive statistics

Standard deviation15307.59773
Coefficient of variation (CV)0.5047094035
Kurtosis1232.734446
Mean30329.5275
Median Absolute Deviation (MAD)8452
Skewness9.621228551
Sum3.336248025 × 1010
Variance234322548.3
MonotonicityNot monotonic
2022-09-05T01:42:17.118321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179952012
 
0.2%
189951969
 
0.2%
199951960
 
0.2%
169951947
 
0.2%
159951621
 
0.1%
149951516
 
0.1%
175001513
 
0.1%
209951487
 
0.1%
219951445
 
0.1%
165001442
 
0.1%
Other values (68793)1083088
98.5%
ValueCountFrequency (%)
4843
< 0.1%
4981
 
< 0.1%
5001
 
< 0.1%
6501
 
< 0.1%
7491
 
< 0.1%
7501
 
< 0.1%
7621
 
< 0.1%
7771
 
< 0.1%
7951
 
< 0.1%
8002
< 0.1%
ValueCountFrequency (%)
26985001
< 0.1%
21750001
< 0.1%
9999951
< 0.1%
9885121
< 0.1%
5795251
< 0.1%
5445001
< 0.1%
4499951
< 0.1%
4185001
< 0.1%
4149911
< 0.1%
4056751
< 0.1%

transmission
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
A
885828 
CVT
197889 
M
 
13307
Dual Clutch
 
2976

Length

Max length11
Median length1
Mean length1.386852727
Min length1

Characters and Unicode

Total characters1525538
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A885828
80.5%
CVT197889
 
18.0%
M13307
 
1.2%
Dual Clutch2976
 
0.3%

Length

2022-09-05T01:42:17.265578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T01:42:17.398487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a885828
80.3%
cvt197889
 
17.9%
m13307
 
1.2%
dual2976
 
0.3%
clutch2976
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A885828
58.1%
C200865
 
13.2%
V197889
 
13.0%
T197889
 
13.0%
M13307
 
0.9%
u5952
 
0.4%
l5952
 
0.4%
D2976
 
0.2%
a2976
 
0.2%
2976
 
0.2%
Other values (3)8928
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1498754
98.2%
Lowercase Letter23808
 
1.6%
Space Separator2976
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A885828
59.1%
C200865
 
13.4%
V197889
 
13.2%
T197889
 
13.2%
M13307
 
0.9%
D2976
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
u5952
25.0%
l5952
25.0%
a2976
12.5%
t2976
12.5%
c2976
12.5%
h2976
12.5%
Space Separator
ValueCountFrequency (%)
2976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1522562
99.8%
Common2976
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A885828
58.2%
C200865
 
13.2%
V197889
 
13.0%
T197889
 
13.0%
M13307
 
0.9%
u5952
 
0.4%
l5952
 
0.4%
D2976
 
0.2%
a2976
 
0.2%
t2976
 
0.2%
Other values (2)5952
 
0.4%
Common
ValueCountFrequency (%)
2976
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1525538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A885828
58.1%
C200865
 
13.2%
V197889
 
13.0%
T197889
 
13.0%
M13307
 
0.9%
u5952
 
0.4%
l5952
 
0.4%
D2976
 
0.2%
a2976
 
0.2%
2976
 
0.2%
Other values (3)8928
 
0.6%

transmission_display
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
Automatic
468215 
Continuously Variable Transmission
197207 
6-Speed Automatic
170695 
8-Speed Automatic
132227 
9-Speed Automatic
77976 
Other values (25)
53680 

Length

Max length34
Median length27
Mean length16.67471091
Min length6

Characters and Unicode

Total characters18342182
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row6-Speed Automatic
2nd row6-Speed Automatic
3rd row9-Speed Automatic
4th row6-Speed Automatic
5th row8-Speed Automatic

Common Values

ValueCountFrequency (%)
Automatic468215
42.6%
Continuously Variable Transmission197207
17.9%
6-Speed Automatic170695
 
15.5%
8-Speed Automatic132227
 
12.0%
9-Speed Automatic77976
 
7.1%
6-Speed Automatic Overdrive11256
 
1.0%
5-Speed Automatic9500
 
0.9%
4-Speed Automatic7821
 
0.7%
7-Speed Automatic7686
 
0.7%
6-Speed Manual6830
 
0.6%
Other values (20)10587
 
1.0%

Length

2022-09-05T01:42:17.503252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
automatic885828
45.7%
variable197207
 
10.2%
transmission197207
 
10.2%
continuously197207
 
10.2%
6-speed190088
 
9.8%
8-speed133448
 
6.9%
9-speed77985
 
4.0%
manual13307
 
0.7%
overdrive11456
 
0.6%
5-speed10947
 
0.6%
Other values (8)23573
 
1.2%

Most occurring characters

ValueCountFrequency (%)
t1971839
 
10.8%
i1686112
 
9.2%
a1507039
 
8.2%
o1477449
 
8.1%
u1299501
 
7.1%
m1083035
 
5.9%
e1078933
 
5.9%
c888804
 
4.8%
A885828
 
4.8%
838253
 
4.6%
Other values (27)5625389
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14705246
80.2%
Uppercase Letter1939617
 
10.6%
Space Separator838253
 
4.6%
Decimal Number429659
 
2.3%
Dash Punctuation429407
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1971839
13.4%
i1686112
11.5%
a1507039
10.2%
o1477449
10.0%
u1299501
8.8%
m1083035
7.4%
e1078933
7.3%
c888804
6.0%
n802135
 
5.5%
s788828
 
5.4%
Other values (8)2121571
14.4%
Decimal Number
ValueCountFrequency (%)
6190088
44.2%
8133448
31.1%
977985
18.2%
510947
 
2.5%
78835
 
2.1%
47843
 
1.8%
1254
 
0.1%
0252
 
0.1%
37
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A885828
45.7%
S429407
22.1%
C200865
 
10.4%
T197889
 
10.2%
V197889
 
10.2%
M13307
 
0.7%
O11456
 
0.6%
D2976
 
0.2%
Space Separator
ValueCountFrequency (%)
838253
100.0%
Dash Punctuation
ValueCountFrequency (%)
-429407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16644863
90.7%
Common1697319
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1971839
11.8%
i1686112
10.1%
a1507039
 
9.1%
o1477449
 
8.9%
u1299501
 
7.8%
m1083035
 
6.5%
e1078933
 
6.5%
c888804
 
5.3%
A885828
 
5.3%
n802135
 
4.8%
Other values (16)3964188
23.8%
Common
ValueCountFrequency (%)
838253
49.4%
-429407
25.3%
6190088
 
11.2%
8133448
 
7.9%
977985
 
4.6%
510947
 
0.6%
78835
 
0.5%
47843
 
0.5%
1254
 
< 0.1%
0252
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18342182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1971839
 
10.8%
i1686112
 
9.2%
a1507039
 
8.2%
o1477449
 
8.1%
u1299501
 
7.1%
m1083035
 
5.9%
e1078933
 
5.9%
c888804
 
4.8%
A885828
 
4.8%
838253
 
4.6%
Other values (27)5625389
30.7%

wheel_system
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
FWD
468267 
AWD
298310 
4WD
247006 
RWD
47690 
4X2
 
38727

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3300000
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFWD
2nd rowAWD
3rd rowFWD
4th rowAWD
5th row4WD

Common Values

ValueCountFrequency (%)
FWD468267
42.6%
AWD298310
27.1%
4WD247006
22.5%
RWD47690
 
4.3%
4X238727
 
3.5%

Length

2022-09-05T01:42:17.618589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T01:42:17.733511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
fwd468267
42.6%
awd298310
27.1%
4wd247006
22.5%
rwd47690
 
4.3%
4x238727
 
3.5%

Most occurring characters

ValueCountFrequency (%)
W1061273
32.2%
D1061273
32.2%
F468267
14.2%
A298310
 
9.0%
4285733
 
8.7%
R47690
 
1.4%
X38727
 
1.2%
238727
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2975540
90.2%
Decimal Number324460
 
9.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W1061273
35.7%
D1061273
35.7%
F468267
15.7%
A298310
 
10.0%
R47690
 
1.6%
X38727
 
1.3%
Decimal Number
ValueCountFrequency (%)
4285733
88.1%
238727
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Latin2975540
90.2%
Common324460
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
W1061273
35.7%
D1061273
35.7%
F468267
15.7%
A298310
 
10.0%
R47690
 
1.6%
X38727
 
1.3%
Common
ValueCountFrequency (%)
4285733
88.1%
238727
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3300000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W1061273
32.2%
D1061273
32.2%
F468267
14.2%
A298310
 
9.0%
4285733
 
8.7%
R47690
 
1.4%
X38727
 
1.2%
238727
 
1.2%

wheelbase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct309
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.6296169
Minimum90.6
Maximum164.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:17.857318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum90.6
5-th percentile100.6
Q1106.3
median111.2
Q3118.1
95-th percentile145
Maximum164.6
Range74
Interquartile range (IQR)11.8

Descriptive statistics

Standard deviation13.18463103
Coefficient of variation (CV)0.1150194111
Kurtosis1.043107448
Mean114.6296169
Median Absolute Deviation (MAD)5.3
Skewness1.427942261
Sum126092578.6
Variance173.8344954
MonotonicityNot monotonic
2022-09-05T01:42:17.993387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.357199
 
5.2%
112.252038
 
4.7%
14550324
 
4.6%
105.134137
 
3.1%
106.531198
 
2.8%
100.630649
 
2.8%
111.228207
 
2.6%
111.427371
 
2.5%
104.725454
 
2.3%
11123969
 
2.2%
Other values (299)739454
67.2%
ValueCountFrequency (%)
90.6238
 
< 0.1%
93.36
 
< 0.1%
93.472
 
< 0.1%
93.5179
 
< 0.1%
94.95
 
< 0.1%
95.4977
0.1%
95.63
 
< 0.1%
96.13
 
< 0.1%
96.51128
0.1%
96.8953
0.1%
ValueCountFrequency (%)
164.660
 
< 0.1%
163.721
 
< 0.1%
163.16
 
< 0.1%
1633
 
< 0.1%
160.37
 
< 0.1%
157.519
 
< 0.1%
157.13
 
< 0.1%
15737
 
< 0.1%
156.82863
0.3%
156.66
 
< 0.1%

width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct231
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.37142236
Minimum62.9
Maximum98.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:18.180408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum62.9
5-th percentile69.9
Q172.6
median77.3
Q382.6
95-th percentile96.8
Maximum98.6
Range35.7
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.249061963
Coefficient of variation (CV)0.09249624091
Kurtosis0.1253460526
Mean78.37142236
Median Absolute Deviation (MAD)4.8
Skewness0.8400589133
Sum86208564.6
Variance52.54889934
MonotonicityNot monotonic
2022-09-05T01:42:18.313277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.853208
 
4.8%
72.447151
 
4.3%
7342242
 
3.8%
69.934062
 
3.1%
70.932527
 
3.0%
73.228975
 
2.6%
81.226976
 
2.5%
8026427
 
2.4%
82.124948
 
2.3%
70.824604
 
2.2%
Other values (221)758880
69.0%
ValueCountFrequency (%)
62.9179
 
< 0.1%
63.71
 
< 0.1%
64.132
 
< 0.1%
65.45
 
< 0.1%
65.61108
0.1%
65.7888
0.1%
65.92
 
< 0.1%
66.118
 
< 0.1%
66.220
 
< 0.1%
66.310
 
< 0.1%
ValueCountFrequency (%)
98.641
 
< 0.1%
97.4441
 
< 0.1%
972004
 
0.2%
96.853208
4.8%
95.717
 
< 0.1%
93.8822
 
0.1%
93.49338
 
0.8%
92.35625
 
0.5%
91.81732
 
0.2%
91.51668
 
0.2%

year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.496676
Minimum1989
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:18.450823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile2014
Q12017
median2020
Q32020
95-th percentile2020
Maximum2021
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.427173766
Coefficient of variation (CV)0.00120246607
Kurtosis7.782431978
Mean2018.496676
Median Absolute Deviation (MAD)0
Skewness-2.321185733
Sum2220346344
Variance5.891172491
MonotonicityNot monotonic
2022-09-05T01:42:18.564692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2020551435
50.1%
2017154762
 
14.1%
2019117682
 
10.7%
201888384
 
8.0%
201643572
 
4.0%
202140716
 
3.7%
201530126
 
2.7%
201421135
 
1.9%
201315915
 
1.4%
201210763
 
1.0%
Other values (22)25510
 
2.3%
ValueCountFrequency (%)
19891
 
< 0.1%
19912
 
< 0.1%
19925
 
< 0.1%
19934
 
< 0.1%
19947
 
< 0.1%
199518
 
< 0.1%
19968
 
< 0.1%
199733
< 0.1%
199853
< 0.1%
199955
< 0.1%
ValueCountFrequency (%)
202140716
 
3.7%
2020551435
50.1%
2019117682
 
10.7%
201888384
 
8.0%
2017154762
 
14.1%
201643572
 
4.0%
201530126
 
2.7%
201421135
 
1.9%
201315915
 
1.4%
201210763
 
1.0%

Torque_lb_ft
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct341
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.8175509
Minimum74
Maximum808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:18.703542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile138
Q1176
median250
Q3305
95-th percentile395
Maximum808
Range734
Interquartile range (IQR)129

Descriptive statistics

Standard deviation87.41140026
Coefficient of variation (CV)0.3499009575
Kurtosis-0.06472948451
Mean249.8175509
Median Absolute Deviation (MAD)69
Skewness0.5414514795
Sum274799306
Variance7640.752895
MonotonicityNot monotonic
2022-09-05T01:42:18.838566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39576196
 
6.9%
17046707
 
4.2%
31036767
 
3.3%
35536451
 
3.3%
13833069
 
3.0%
14731199
 
2.8%
29530043
 
2.7%
18129578
 
2.7%
28026063
 
2.4%
18524705
 
2.2%
Other values (331)729222
66.3%
ValueCountFrequency (%)
7449
 
< 0.1%
781939
0.2%
84179
 
< 0.1%
901
 
< 0.1%
934
 
< 0.1%
961
 
< 0.1%
10045
 
< 0.1%
101167
 
< 0.1%
10312
 
< 0.1%
1044
 
< 0.1%
ValueCountFrequency (%)
80813
 
< 0.1%
797125
 
< 0.1%
76037
 
< 0.1%
717127
 
< 0.1%
707396
< 0.1%
7004
 
< 0.1%
66215
 
< 0.1%
650135
 
< 0.1%
64016
 
< 0.1%
6332
 
< 0.1%

Torque_RPM
Real number (ℝ≥0)

HIGH CORRELATION

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3539.321395
Minimum200
Maximum6800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-09-05T01:42:18.988621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile1400
Q12500
median4000
Q34400
95-th percentile4900
Maximum6800
Range6600
Interquartile range (IQR)1900

Descriptive statistics

Standard deviation1251.836388
Coefficient of variation (CV)0.3536939001
Kurtosis-0.2316823695
Mean3539.321395
Median Absolute Deviation (MAD)500
Skewness-0.9396727868
Sum3893253534
Variance1567094.341
MonotonicityNot monotonic
2022-09-05T01:42:19.123671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000125618
 
11.4%
4400118985
 
10.8%
450076954
 
7.0%
410059262
 
5.4%
470056916
 
5.2%
200055620
 
5.1%
150051633
 
4.7%
480047386
 
4.3%
300044119
 
4.0%
395033724
 
3.1%
Other values (79)429783
39.1%
ValueCountFrequency (%)
20030649
2.8%
23391
 
< 0.1%
32327
 
< 0.1%
40011
 
< 0.1%
100011
 
< 0.1%
12002809
 
0.3%
12504050
 
0.4%
13005523
 
0.5%
13501238
 
0.1%
13701764
 
0.2%
ValueCountFrequency (%)
68003
 
< 0.1%
660035
 
< 0.1%
6400171
 
< 0.1%
610022
 
< 0.1%
60006
 
< 0.1%
58002
 
< 0.1%
57001966
0.2%
560088
 
< 0.1%
550021
 
< 0.1%
540045
 
< 0.1%

Interactions

2022-09-05T01:41:49.016217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-09-05T01:41:45.071723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:54.920103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:14.183441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:23.799509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:33.745313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:43.525756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:53.227359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:02.748773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:11.984545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:28.773314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:38.313060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:47.713748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:56.804898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:06.689275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:17.124022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:26.586663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:36.108218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:45.617575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:55.477503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:14.712996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:24.389305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:34.286853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:44.075427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:53.777253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:03.304630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:12.534465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:29.351592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:38.869002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:48.231559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:57.338516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:07.368847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:17.679843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:27.202046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:36.685165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:46.256304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:56.116323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:15.270982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:25.089879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:34.815369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:44.635819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:54.369304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:03.861079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:13.070721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:29.918495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:39.522197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:48.762760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:57.842129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:08.062077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:18.189947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:27.881082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:37.233408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:46.815958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:56.699616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:15.891776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:25.655588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:35.395420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:45.255674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:54.893181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:04.401114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:13.618790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:30.476371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:40.123800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:49.401750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:58.370345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:08.714905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:18.718664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:28.418696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:37.806822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:47.383280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:57.229361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:16.449765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:26.336959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:35.956881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:45.920489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:55.427655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:04.947851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:14.149588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:31.031914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:40.683857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:49.942929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:58.880124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:09.438840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:19.228684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:28.984092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:38.368019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:47.924319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:57.772216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:17.040489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:26.938000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:36.515569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:46.485701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:39:55.968072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:05.491424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:22.111470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:31.592396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:41.235752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:50.476704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:40:59.544704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:10.029685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:19.760781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:29.550132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:38.964752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T01:41:48.468554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-05T01:42:19.356785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-05T01:42:19.595662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-05T01:42:19.833473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-05T01:42:20.065722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-05T01:42:20.283684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-05T01:41:58.789884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-05T01:42:03.532632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

back_legroombody_typecity_fuel_economydaysonmarketengine_cylindersengine_displacementfranchise_makefront_legroomfuel_tank_volumefuel_typeheighthighway_fuel_economyhorsepoweris_newlengthlisted_datelisting_colorlisting_idmaximum_seatingmileagemodel_namepricetransmissiontransmission_displaywheel_systemwheelbasewidthyearTorque_lb_ftTorque_RPM
038.1Sedan27.055I41500.0Chevrolet42.015.8 galGasoline57.636.0160.0False193.82020-07-16SILVER2766753045 seats42394.0Malibu14639.0A6-Speed AutomaticFWD111.473.020181602500
127.3Coupe18.036V63500.0Jeep45.417.4 galGasoline55.124.0311.0False184.82020-08-04BLACK2783521944 seats62251.0RC 35032000.0A6-Speed AutomaticAWD107.581.520183114800
238.4SUV / Crossover18.027V63600.0Chevrolet41.019.4 galGasoline70.727.0310.0False204.32020-08-13SILVER2791291808 seats36410.0Traverse23723.0A9-Speed AutomaticFWD120.978.620183102800
336.8SUV / Crossover15.027V63600.0Chevrolet41.322 galGasoline69.922.0281.0False203.72020-08-13SILVER2791291818 seats36055.0Traverse22422.0A6-Speed AutomaticAWD118.978.520172813400
438.6SUV / Crossover18.024V63600.0Chevrolet40.324.6 galGasoline69.325.0295.0False189.82020-08-16BLACK2795316215 seats25745.0Grand Cherokee29424.0A8-Speed Automatic4WD114.884.820182954800
539.4SUV / Crossover20.020I42400.0Jeep40.613.5 galGasoline65.025.0172.0False175.12020-08-20BLACK2797750735 seats14607.0Compass17000.0A6-Speed Automatic4WD103.771.420171724400
634.1Coupe27.0102I42000.0Jeep42.613.2 galGasoline55.134.0147.0False166.92020-05-30RED2729509384 seats3073.0Veloster16900.0A6-Speed AutomaticFWD104.370.920201474500
738.6SUV / Crossover18.035V63000.0Jeep40.324.6 galGasoline69.325.0241.0False189.82020-08-05SILVER2784625725 seats16467.0Grand Cherokee25500.0A8-Speed Automatic4WD114.884.820173604250
838.4SUV / Crossover18.041V63600.0Chevrolet41.019.4 galGasoline70.727.0310.0False204.32020-07-30WHITE2779987078 seats37536.0Traverse23939.0A9-Speed AutomaticFWD120.978.620183102800
939.5SUV / Crossover24.0246I42000.0Cadillac40.415.9 galGasoline64.130.0237.0False181.12020-01-07BLACK2624905015 seats6946.0XT431339.0A9-Speed AutomaticFWD109.483.520192371500

Last rows

back_legroombody_typecity_fuel_economydaysonmarketengine_cylindersengine_displacementfranchise_makefront_legroomfuel_tank_volumefuel_typeheighthighway_fuel_economyhorsepoweris_newlengthlisted_datelisting_colorlisting_idmaximum_seatingmileagemodel_namepricetransmissiontransmission_displaywheel_systemwheelbasewidthyearTorque_lb_ftTorque_RPM
109999039.4SUV / Crossover18.010V63300.0Buick44.118.8 galGasoline66.525.0290.0False187.42020-09-01BLACK2808857927 seats32652.0Sorento29900.0AAutomaticFWD109.474.420182905300
109999135.7SUV / Crossover26.0342I41400.0Chevrolet40.814 galGasoline66.031.0138.0True167.62019-10-05WHITE2543562065 seats4043.0Trax19087.0A6-Speed AutomaticFWD100.669.92020138200
109999235.8Pickup Truck18.031V63600.0Chevrolet45.021 galGasoline70.725.0308.0True212.72020-08-11WHITE2789651925 seats3.0Colorado29268.0A8-Speed Automatic4X2128.383.920213084000
109999338.1Sedan27.0499I41500.0Chevrolet42.015.8 galGasoline57.636.0160.0False193.82019-05-01RED2395020655 seats4165.0Malibu18465.0A6-Speed AutomaticFWD111.473.020181602500
109999435.7SUV / Crossover26.0339I41400.0Chevrolet40.814 galGasoline66.031.0138.0True167.62019-10-08UNKNOWN2545390545 seats7.0Trax19087.0A6-Speed AutomaticFWD100.669.92020138200
109999543.6Pickup Truck15.013V8 Flex Fuel Vehicle5000.0Ford43.926 galFlex Fuel Vehicle77.221.0395.0True231.92020-08-29WHITE2807321075 seats3.0F-15061577.0AAutomatic4WD145.096.820203753500
109999645.2Pickup Truck15.01V85700.0RAM40.926 galGasoline77.522.0395.0True232.92020-09-10BLUE2817716126 seats20.0150041595.0AAutomatic4X2144.682.120203953950
109999738.6SUV / Crossover14.014V85700.0Dodge40.324.6 galGasoline71.922.0360.0True201.22020-08-28GRAY2805787427 seats22.0Durango44991.0AAutomaticRWD119.885.520203604250
109999838.1Sedan25.030I41800.0Volkswagen41.214.5 galGasoline57.235.0170.0False183.32020-08-12RED2790615995 seats31490.0Jetta16969.0A6-Speed AutomaticFWD104.470.020171701500
109999930.3Coupe17.045V63800.0Chevrolet44.117.2 galGasoline54.524.0348.0False182.32020-07-28SILVER2777267524 seats39076.0Genesis Coupe17992.0M6-Speed ManualRWD111.073.420163485100